Estimating Building-Scale Operation Carbon Emissions of Different Building Types: A Case Study of Guiyang

Understanding building operational carbon dioxide (CO2) emissions is essential for sustainable urban planning, yet variations in emissions across building types remain poorly characterized. This study integrated top-down and bottom-up approaches to estimate building-scale CO2 emissions, capturing fine-scale emission patterns while maintaining consistency with aggregate energy statistics. Taking Guiyang as a case study, electricity consumption was simulated using the Designer’s Simulation Tool (DeST), while natural gas (NG) and liquefied petroleum gas (LPG) consumption were disaggregated using an area-proportional allocation method. Emission factors were then applied to estimate monthly CO2 emissions. Results showed that monthly building CO2 emissions ranged from 0.81 to 1.09 million tons. Significant spatial disparities were observed, with core districts contributing more than 22% of total emissions, whereas peripheral districts accounted for only approximately 6%. Residential buildings produced the highest total emissions, averaging 433 thousand tons per month, while shopping malls showed the highest emission intensity, reaching 8.08 kg/m2 in July. The different building types and different seasons had different emissions, with residential buildings showing higher emissions in winter, whereas hotels and shopping malls experienced higher emissions during summer. The proposed framework extends existing approaches, providing reliable building CO2 data for urban low-carbon planning and targeted mitigation.